DDD Blog
Our thoughts and insights on machine learning and artificial intelligence applications
Welcome to Digital Divide Data’s (DDD) blog, fully dedicated to Machine Learning trends and resources, new data technologies, data training experiences, and the latest news in the areas of Deep Learning, Optical Character Recognition, Computer Vision, Natural Learning Processing, and more.
For Artificial Intelligence (AI) professionals, adding the latest machine learning blog or two to your reading list will help you get updates on industry news and trends.
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The Art of Data Annotation in Machine Learning
Data Annotation has become a cornerstone in the development of AI and ML models. In this blog, we will explore more about data annotation and its use cases in machine learning.
Determining The New Gold Standard of Autonomous Driving
Emerging standards are beginning to regulate how manufacturers approach navigation, safety, and AD modeling quality. These standards also influence policy creation, technology use, and the general framework for AD systems. Creating standard systems for these AD models will lead to a more uniform approach toward autonomous driving models.
4 Advantages of Human-Powered Data Annotation vs Tools/Software
Once you've created a clean training data set for supervised learning, the story isn't over. Human intervention is needed to assess how well the AI can correctly identify diseased crops in the future.
Why Data Annotation Software Still Needs a Human Touch
"Although AI has advanced enormously over the past decade, involving humans in its development is still essential if premium results are required.
Here we take a look at how AI is trained using test data and how human-powered data annotation and data labeling adds significant value to the outcomes that AI delivers. "
Data Bias: AI’s Ticking Time Bomb
We’ve all seen the headlines. It’s big news when an AI system fails or backfires, and it’s an awful black eye for the organization the headlines point to. Most of the time these headlines can be traced back to issues with the AI model’s training data.
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